The present invention relates generally to a method for detecting a focus condition for use in optical devices such as a camera and microscope, and more particularly to a focus detection method which utilizes a surface acoustic wave device for effecting a Fourier transform on real time.
As to a method for detecting the focus condition of an imaging optical system, there has been proposed a method for detecting an image sharpness by Fourier-transforming an image signal and comparing its spatial frequency component. In this method, a part of the image of an object is transformed into a time-frequency series by a solid state line sensor, and then the Fourier transform is performed for the thus transformed image. After that, an amount of integrated high frequency components thereof is compared with a predetermined value so as to derive a signal representing a focus condition. However, in the focus detection method mentioned above, since the Fourier transform can not be effected on real time, only a simple pseudo Fourier transform is effected by means of a so-called contrast echo method. Therefore, according to the method mentioned above, a detection accuracy is low and a detection error occurs largely. In this case, it is possible to use a fast Fourier transform device for effecting the Fourier transform after converting the outputs of the line sensors into digital values. However, even if the Fourier transform can be effected in a few hundred ms, the whole apparatus is made large in size and expensive in cost, and thus it is not possible to accommodate it in a small space of an optical machine such as a camera.
As to the other focus detection method, there has been proposed a method that two images formed by two light fluxes passing through two different regions of an exit pupil of the imaging lens are received respectively by two light receiving element arrays each comprising a plurality of light receiving elements, and then a correlation between images formed on the two light receiving element arrays i.e. a lateral shift of the two images is detected so as to derive the focus condition. However, in this known method, since the correlation of the images is detected by calculating image signals according to a predetermined algorithm after converting outputs of the two light receiving element arrays into digital values, the calculation requires an extremely long time period and thus the focus information is not obtained quickly.
Recently, there has been developed a surface acoustic wave device of an analog type which performs the Fourier transform or a convolution of two signals by utilizing the surface acoustic waves. Such surface acoustic wave element can perform the signal processing on real time and also can be made small to such an extent that the whole apparatus can be easily accommodated in an IC package.
Hereinafter, the surface acoustic wave element will be explained with reference to a Japanese Magazine "Television Gakkai-shi, vol. 36, No. 6 (1982), pp. 498-504".
FIG. 1A is a schematic view showing one embodiment of a chirp filter using the surface acoustic wave element. A chirp filter 1 of an interdigital type comprises a piezoelectric substrate 2 such as PZT or LiNbO.sub.3, a first interdigital electrode 3 for transmitting a surface acoustic wave and a second interdigital electrode 4 for receiving the surface acoustic wave. These interdigital electrodes 3 and 4 are arranged on the substrate 2 apart from each other. The second interdigital electrode 4 has a variable pitch so that a delay time varies in proportion to the frequency of the surface acoustic wave. This chirp filter 1 can be utilized for effecting the Fourier transform.
If it is assumed that a frequency band width of an input signal is B, a length of the chirp filter 1 is l, a propagating velocity of the surface acoustic wave on the chirp filter is v and a resolution of the Fourier transform in N, the resolution N is approximated to two times a compression ratio lB/v of the chirp filter 1. Therefore, a relation l=Nv/2B is derived from N=2lB/v. Here, if it is further assumed that the frequency band width B is 6 MHz, the piezoelectric substrate 2 is PZT and v.congruent.2,000 m/sec, in order to attain the resolution N=100, the length of the chirp filter 1 is derived as l=16.7 mm from the equation mentioned above and this dimension is extremely small. In addition, since a time period t required for effecting the Fourier transform is equal to a period during which the signal wave travels across the chirp filter 1, and this time period t is made extremely small because t is calculated as t=l/v.congruent.8.4 .mu.s, and thus it is possible to effect the Fourier transform on real time. In the calculation mentioned above, use is made of the assumption of N=100 wherein a spectrum band of the Fourier transform is divided into 100 regions, but it is possible to effect the focus detection if the resolution is made lower than N=100. Then, the device can be made much smaller and the Fourier transform can be performed within a shorter time. In addition, since it is possible to use a substrate having a low sonic velocity such as LiNbO.sub.3 instead of the piezoelectric substrate 2, it is possible to make the dimension of the chirp filter 1 even smaller, if necessary. Moreover, it is possible to use the chirp filter of reflective-array compressor type having the variable pitch construction, and in this case the length of the chirp filter can be made one half of that of the aforementioned interdigital type.
FIGS. 1B and 1C are schematic views showing one embodiment of the Fourier transform device using a plurality of the chirp filters mentioned above. In FIG. 1B, a Fourier transform device 5 comprises two chirp filters 6-1 and 6-2 having an impulse response for an up-chirp signal wherein the frequency is increased linearly with respect to the time, and a chirp filter 7 having an impulse response for a down-chirp signal wherein the frequency is decreased linearly with respect to the time. In this Fourier transform device, an input signal and the up-chirp signal supplied from the chirp filter 6-1 are multiplied in a multiplier 8-1, and the thus multiplied signal is supplied to the chirp filter 7. Further, an output signal of the chirp filter 7 and the down-chirp signal supplied from the chirp filter 6-2 are multiplied in a multiplier 8-2. In this manner, an output signal after effecting the Fourier transform is derived. Moreover, in FIG. 1C, a Fourier transform device 9 comprises the chirp filter 6-1 generating the up-chirp signal and the chirp filter 7 generating the down-chirp signal, and as compared with the embodiment shown in FIG. 1B the chirp filter 6-2 is eliminated. As mentioned above, since the Fourier transform device is constituted of the chirp filters which utilize the surface acoustic waves, the Fourier transform can be performed accurately on real time and also the device can be made extremely small in size.
FIG. 2A is a perspective view showing one embodiment of a convolver which utilizes the surface acoustic wave devices. In FIG. 2A, a convolver 11 comprises two interdigital electrodes 13-1 and 13-2 arranged apart from each other on a piezoelectric substrate 12, a semiconductor 14 arranged on one surface of the substrate 12 substantially at a middle between these electrodes 13-1 and 13-2, and a bottom metal plate 15 arranged on the other surface of the substrate 12 opposite to the semiconductor 14. In this embodiment, the surface acoustic waves simultaneously transmitted from the two electrodes 13-1 and 13-2 in opposite directions interfere with each other due to the acoustic nonlinearity of the semiconductor 14, and thus it is possible to obtain an output signal representing the convolution of the two input signals from the semiconductor 14. In this convolver 11, if it is assumed that an interference length of the semiconductor 14 is 1.5T, and to the electrodes 13-1 and 13-2 are supplied signal waves having a band width B and time durations of 2T and T, respectively, at a timing shown in FIG. 2B, the convoluting operations for N=B.times.T points can be effected within the time duration of 2T as shown in FIG. 2C, and thus it is possible to derive the correlation between the two input signals.
As to the method for detecting the focus condition by utilizing the surface acoustic wave, there has been proposed a method and an apparatus in U.S. Pat. No. 4,053,934. In this known method, an image to be observed is projected onto a CdS film and at the same time the surface acoustic waves having a predetermined frequency are applied to the CdS film by means of a transducer. Then, an image sharpness is derived from the Fourier transform component corresponding to the frequency of the surface acoustic wave applied to the CdS film. In this case, since the image sharpness is derived from an average of the whole image to be observed, the detection accuracy is low, particularly for a certain kind of image. Moreover, according to the known method, it is not possible to detect the focus condition in a specific region of the image. Further, since the focus detection is performed only by the image sharpness detection method, the high detection accuracy could not be obtained for a low contrast image such as a human face.
The inventors have recognized the fact that in the known focus detection method utilizing the surface acoustic wave, the surface acoustic wave devices explained above have not be used and further that if the surface acoustic wave devices are utilized optionally, it is possible to effect the focus detection very accurately.